Integrated AI and Remote Sensing Framework for Sustainable Agriculture and Environmental Monitoring

Abstract

The growing global food production demand along with rapidly accelerating consequences from climate change, continued environmental degradation, and scarce natural resources have resulted in a great urgency for intelligent agricultural management systems. Conventional agricultural systems rely heavily on regular field phonological check-ups and seasonal environmental monitoring, which do not deliver timely or accurate information for informed sustainable decision-making. ABSTRACT Recent developments in artificial intelligence (AI), remote sensing technologies, unscrewed aerial vehicles (UAVs), satellite imaging, Internet of Things(IoT) devices, cloud computing, and geospatial analytics have opened wider scope for the transition of agricultural production as well as environmental monitoring to extremely intelligent data-driven operations. Through the integration of these emerging technologies, it is possible to monitor crop health status and soil properties together with weather conditions temperature and transfer moisture, water resources, and vegetation dynamics, biodiversity, changes in ecosystems over large areas at higher spatial-temporal resolution. This study presents a framework for integrated AI and remote sensing tools, intended to assist sustainable agriculture by means of intelligent data acquisition/sensing, preprocessing techniques, feature extraction methodologies, predictive analytics algorithms, and decision-support systems. In this paper, a framework is proposed based on multispectral satellite imagery and data from hyperspectral sensing techniques (UAVs), ground sensor networks, machine learning algorithms/deep-learn architectures; GIS & cloud-based analytical platforms. This aims to generate precise recommendations in small time scales for precision farming as well as for the protection of environmental habitats. The new framework would help with early detection of crop diseases, optimal irrigation scheduling, and prediction of agricultural yield or production capacity for example the best planting areas, land use change monitoring and carbon sequestration estimation as well as drought severity assessment & biodiversity conservation efforts. The review integrates the conceptual frameworks underlying AI and precision agriculture, remote sensing applications of electronic data in environmental analytics, intelligent decision-support systems, and current technological limitations and research gaps. In addition, an innovative research methodology is conceived to assess the performance of multidimensional indicators (prediction accuracy & computational efficiency), and new yardsticks for measuring scalability along with environmental impact/recyclability will be utilized. Comparative analysis shows that AI for multisource remote sensing contains increased agricultural productivity, environmental sustainability, as well as operational savings in terms of lower chemical usage and climate-resilient practices. The results suggest that hybrid AI models combining convolutional neural networks (CNNs), recurrent neural network (RNN) in landscape features, transformer architectures in area plots with ensemble learning and explainable artificial intelligence for interpreting top items outperform traditional statistical methods based on heterogeneous geospatial datasets. The ability to enable cloud-based big data infrastructures now also permits near real-time monitoring over large agricultural areas whilst enhancing compatibility between satellite systems, inter-networked devices and government decision-support platforms. The proposed framework advances sustainable digital agriculture by offering a scalable, automated intelligent solution that can be adopted for emerging agricultural and environmental challenges in varying climates and geographies.